Learning History via CYOA

Hacker News

I made Amnesia, a generative AI choose-your-own-adventure game that drops you in any given time and place and lets you explore. I’ve tried a lot of these kinds of games before and have always found that the lack of structure and intention in “you can do anything” AI games prevents me from caring about the content of the story. I thought it might be interesting to experiment with using a narrative to consume content I would’ve cared about otherwise, which led me to making Amnesia. Amnesia has two screens- one for the storyline and another for learnings. Every decision moves the narrative and presents you with new learnings. Admittedly, the narrative is pretty weak at the moment, and will need some more work. Having said that, I’ve loved messing around with this and exploring all sorts of time periods and places in a more engaging & personal way. I’d love for you to try it out and share your thoughts!

Visit product View on Hacker NewsJun 12, 2024

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Actual performance

3points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, using · Missing: mac, agents, macos

81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

70%70% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs

60%60% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit

13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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